Authors:Abbas H. Hassin Alasadi, Moqdad Hanon DawoodPages: 207 - 225Abstract: Biometrics is the important area of distinguishing people using their behavioural characteristics. Until now, researcher and exporter increasing interest with vein pattern biometrics. A vein pattern is a massive link of blood vessels under a person's skin. Similar to fingerprints, in scientific sense, the shape of vascular patterns in the same part of the body has proved distinct from each other. The objective of this paper is to analyse vein images and to design and implement dorsal hand-vein recognition system that has the ability to segment vein and recognise each person based on his vein requires the presence of the human operator. The experimental results indicate that the MDC classifier achieves accuracy of 92% in the case of wavelet transform, and GLCM and Tamura features.Keywords: biometric; vein images; feature extraction; DWT; grey level co-occurrence matrix; GLCM; TamuraCitation: International Journal of Applied Pattern Recognition, Vol. 4, No. 3 (2017) pp. 207 - 225PubDate: 2017-09-12T23:20:50-05:00DOI: 10.1504/IJAPR.2017.086586Issue No:Vol. 4, No. 3 (2017)

Authors:Abbas H. Hassin Alasadi, Moqdad Hanon DawoodPages: 226 - 245Abstract: This paper proposes a novel edge detection algorithm based on model fitting and the calculation of the first order derivative of two-dimensional images. It is central to mention that the aforementioned calculation can only be done through approximations since the image being used is made of a sequel of discrete samples, and the continuity is brought in through the model function which needs to offer the property of first order differentiability. The cubic bivariate polynomial was used in this paper. Indeed, the partial first order derivatives of the images are calculated after fitting the model function to the image data. It is found that the cubic bivariate polynomial shows visible edges of the images as seen in the results section. The usefulness of the methodology is also tested on a range of images, practical applications, and performance analysis under realistic conditions.Keywords: cubic bivariate polynomial; edge finding; first-order derivative; motion artefacts; non-invasive; model-fitting; biomedical imaging; magnetic resonance imaging; edge-contour; two-dimensional images; edge detectionCitation: International Journal of Applied Pattern Recognition, Vol. 4, No. 3 (2017) pp. 226 - 245PubDate: 2017-09-12T23:20:50-05:00DOI: 10.1504/IJAPR.2017.086595Issue No:Vol. 4, No. 3 (2017)

Authors:Jayant Jagtap, Manesh KokarePages: 246 - 260Abstract: Human face shows different patterns on the face for particular age class. These ageing patterns on the human faces can be directly used for developing age based systems. Computer based age classification via face images become an interesting topic recently because of their real world applications such as electronic customer relationship management, security and surveillance, etc. In this paper, the detailed survey has been made on the techniques and algorithms developed by researchers for age classification via face images. Comparative study has been done in this paper on existing age classification methods on the basis of pre-processing techniques, ageing feature extraction methods, age classifiers, system performances and databases used for evaluation of system. Future research directions related to the topic of automatic human age classification via face images are also mentioned in this paper.Keywords: human face; ageing patterns; age based systems; age classification; ageing process; surveyCitation: International Journal of Applied Pattern Recognition, Vol. 4, No. 3 (2017) pp. 246 - 260PubDate: 2017-09-12T23:20:50-05:00DOI: 10.1504/IJAPR.2017.086594Issue No:Vol. 4, No. 3 (2017)

Authors:Muhammad Waseem Khan, Muhammad Sharif, Mussarat Yasmin, Tanzila SabaPages: 261 - 306Abstract: Glaucoma is an eye disease that occurs when circulation of an eye fluid does not remain normal which in turn increases the intraocular pressure (IOP) in aqueous humour of the human eye. This rise of IOP ultimately damages the optic nerve of an eye and leads to complete or partial vision loss. Several image processing techniques have been developed for the detection of glaucoma on the basis of features such as an optic disc (OD), cup to disc ratio (CDR), retinal nerve fibre layer (RNFL) loss, per papillary atrophy (PPA) and neuroretinal rim loss. This paper presents a review of latest work on the use of the model and non-model approaches to detect glaucoma using OD and CDR as glaucoma features.Keywords: glaucoma; optic disc; cup to disc ratio; CDR; fundus images; CDR detection techniques; wavelet transform; active shape model; ASMCitation: International Journal of Applied Pattern Recognition, Vol. 4, No. 3 (2017) pp. 261 - 306PubDate: 2017-09-12T23:20:50-05:00DOI: 10.1504/IJAPR.2017.086596Issue No:Vol. 4, No. 3 (2017)